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Selected topics in machine learning and data mining, including clustering, neural networks, support vector machines, decision trees. Methods include feature selection and dimensionality reduction, error estimation and empirical validation, algorithm design and parallelization, and handling of large data sets. Emphasis on good methods and practices for deployment of real systems.

Prerequisite(s): MATH 323 or ECSE 205 or ECSE 305 or equivalent
Restriction(s): Not open to students who have taken COMP 598 when topic was "Applied Machine Learning"
Some background in Artificial Intelligence is recommended, e.g. COMP-424 or ECSE-526, but not required.

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